AUTHOR=Muñoz Laguna Javier , Puhan Milo A. , Rodríguez Artalejo Fernando , De Pauw Robby , Wyper Grant M. A. , Devleesschauwer Brecht , Santos João V. , Hincapié Cesar A. 

TITLE=Certainty of the Global Burden of Disease 2019 Modelled Prevalence Estimates for Musculoskeletal Conditions: A Meta-Epidemiological Study

JOURNAL=International Journal of Public Health

VOLUME=68

YEAR=2023

URL=https://www.ssph-journal.org/journals/international-journal-of-public-health/articles/10.3389/ijph.2023.1605763

DOI=10.3389/ijph.2023.1605763

ISSN=1661-8564

ABSTRACT=<p><bold>Objectives:</bold> To describe and assess the risk of bias of the primary input studies that underpinned the Global Burden of Disease Study (GBD) 2019 modelled prevalence estimates of low back pain (LBP), neck pain (NP), and knee osteoarthritis (OA), from Australia, Brazil, Canada, Spain, and Switzerland. To evaluate the certainty of the GBD modelled prevalence evidence.</p><p><bold>Methods:</bold> Primary studies were identified using the GBD Data Input Sources Tool and their risk of bias was assessed using a validated tool. We rated the certainty of modelled prevalence estimates based on the GRADE Guidelines 30―the GRADE approach for modelled evidence.</p><p><bold>Results:</bold> Seventy-two primary studies (LBP: 67, NP: 2, knee OA: 3) underpinned the GBD estimates. Most studies had limited representativeness of their study populations, used suboptimal case definitions and applied assessment instruments with unknown psychometric properties. The certainty of modelled prevalence estimates was low, mainly due to risk of bias and indirectness.</p><p><bold>Conclusion:</bold> Beyond the risk of bias of primary input studies for LBP, NP, and knee OA in GBD 2019, the certainty of country-specific modelled prevalence estimates still have room for improvement.</p>